Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Monday 15 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 145,953 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 145,943 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 30
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 16
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 15
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 4
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 4
## 102 2020-06-10 East of England 7
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 5
## 105 2020-06-13 East of England 2
## 106 2020-03-01 London 0
## 107 2020-03-02 London 0
## 108 2020-03-03 London 0
## 109 2020-03-04 London 0
## 110 2020-03-05 London 0
## 111 2020-03-06 London 1
## 112 2020-03-07 London 1
## 113 2020-03-08 London 0
## 114 2020-03-09 London 1
## 115 2020-03-10 London 0
## 116 2020-03-11 London 7
## 117 2020-03-12 London 6
## 118 2020-03-13 London 10
## 119 2020-03-14 London 14
## 120 2020-03-15 London 10
## 121 2020-03-16 London 18
## 122 2020-03-17 London 25
## 123 2020-03-18 London 31
## 124 2020-03-19 London 25
## 125 2020-03-20 London 44
## 126 2020-03-21 London 50
## 127 2020-03-22 London 54
## 128 2020-03-23 London 64
## 129 2020-03-24 London 87
## 130 2020-03-25 London 113
## 131 2020-03-26 London 130
## 132 2020-03-27 London 130
## 133 2020-03-28 London 122
## 134 2020-03-29 London 147
## 135 2020-03-30 London 150
## 136 2020-03-31 London 181
## 137 2020-04-01 London 202
## 138 2020-04-02 London 190
## 139 2020-04-03 London 196
## 140 2020-04-04 London 230
## 141 2020-04-05 London 195
## 142 2020-04-06 London 198
## 143 2020-04-07 London 219
## 144 2020-04-08 London 238
## 145 2020-04-09 London 206
## 146 2020-04-10 London 170
## 147 2020-04-11 London 177
## 148 2020-04-12 London 158
## 149 2020-04-13 London 166
## 150 2020-04-14 London 144
## 151 2020-04-15 London 142
## 152 2020-04-16 London 139
## 153 2020-04-17 London 100
## 154 2020-04-18 London 101
## 155 2020-04-19 London 103
## 156 2020-04-20 London 95
## 157 2020-04-21 London 95
## 158 2020-04-22 London 108
## 159 2020-04-23 London 77
## 160 2020-04-24 London 71
## 161 2020-04-25 London 58
## 162 2020-04-26 London 53
## 163 2020-04-27 London 51
## 164 2020-04-28 London 43
## 165 2020-04-29 London 44
## 166 2020-04-30 London 40
## 167 2020-05-01 London 41
## 168 2020-05-02 London 40
## 169 2020-05-03 London 36
## 170 2020-05-04 London 30
## 171 2020-05-05 London 25
## 172 2020-05-06 London 37
## 173 2020-05-07 London 37
## 174 2020-05-08 London 29
## 175 2020-05-09 London 23
## 176 2020-05-10 London 26
## 177 2020-05-11 London 18
## 178 2020-05-12 London 18
## 179 2020-05-13 London 16
## 180 2020-05-14 London 20
## 181 2020-05-15 London 18
## 182 2020-05-16 London 14
## 183 2020-05-17 London 15
## 184 2020-05-18 London 9
## 185 2020-05-19 London 13
## 186 2020-05-20 London 19
## 187 2020-05-21 London 12
## 188 2020-05-22 London 10
## 189 2020-05-23 London 6
## 190 2020-05-24 London 7
## 191 2020-05-25 London 9
## 192 2020-05-26 London 12
## 193 2020-05-27 London 7
## 194 2020-05-28 London 8
## 195 2020-05-29 London 7
## 196 2020-05-30 London 12
## 197 2020-05-31 London 6
## 198 2020-06-01 London 10
## 199 2020-06-02 London 7
## 200 2020-06-03 London 6
## 201 2020-06-04 London 8
## 202 2020-06-05 London 3
## 203 2020-06-06 London 0
## 204 2020-06-07 London 4
## 205 2020-06-08 London 5
## 206 2020-06-09 London 2
## 207 2020-06-10 London 7
## 208 2020-06-11 London 5
## 209 2020-06-12 London 0
## 210 2020-06-13 London 0
## 211 2020-03-01 Midlands 0
## 212 2020-03-02 Midlands 0
## 213 2020-03-03 Midlands 1
## 214 2020-03-04 Midlands 0
## 215 2020-03-05 Midlands 0
## 216 2020-03-06 Midlands 0
## 217 2020-03-07 Midlands 0
## 218 2020-03-08 Midlands 3
## 219 2020-03-09 Midlands 1
## 220 2020-03-10 Midlands 0
## 221 2020-03-11 Midlands 2
## 222 2020-03-12 Midlands 6
## 223 2020-03-13 Midlands 5
## 224 2020-03-14 Midlands 4
## 225 2020-03-15 Midlands 5
## 226 2020-03-16 Midlands 11
## 227 2020-03-17 Midlands 8
## 228 2020-03-18 Midlands 13
## 229 2020-03-19 Midlands 8
## 230 2020-03-20 Midlands 28
## 231 2020-03-21 Midlands 13
## 232 2020-03-22 Midlands 31
## 233 2020-03-23 Midlands 33
## 234 2020-03-24 Midlands 41
## 235 2020-03-25 Midlands 48
## 236 2020-03-26 Midlands 64
## 237 2020-03-27 Midlands 72
## 238 2020-03-28 Midlands 89
## 239 2020-03-29 Midlands 92
## 240 2020-03-30 Midlands 90
## 241 2020-03-31 Midlands 123
## 242 2020-04-01 Midlands 140
## 243 2020-04-02 Midlands 142
## 244 2020-04-03 Midlands 124
## 245 2020-04-04 Midlands 151
## 246 2020-04-05 Midlands 164
## 247 2020-04-06 Midlands 140
## 248 2020-04-07 Midlands 123
## 249 2020-04-08 Midlands 186
## 250 2020-04-09 Midlands 139
## 251 2020-04-10 Midlands 127
## 252 2020-04-11 Midlands 142
## 253 2020-04-12 Midlands 139
## 254 2020-04-13 Midlands 120
## 255 2020-04-14 Midlands 116
## 256 2020-04-15 Midlands 147
## 257 2020-04-16 Midlands 102
## 258 2020-04-17 Midlands 118
## 259 2020-04-18 Midlands 115
## 260 2020-04-19 Midlands 92
## 261 2020-04-20 Midlands 107
## 262 2020-04-21 Midlands 86
## 263 2020-04-22 Midlands 78
## 264 2020-04-23 Midlands 103
## 265 2020-04-24 Midlands 79
## 266 2020-04-25 Midlands 72
## 267 2020-04-26 Midlands 81
## 268 2020-04-27 Midlands 74
## 269 2020-04-28 Midlands 68
## 270 2020-04-29 Midlands 53
## 271 2020-04-30 Midlands 56
## 272 2020-05-01 Midlands 64
## 273 2020-05-02 Midlands 51
## 274 2020-05-03 Midlands 52
## 275 2020-05-04 Midlands 61
## 276 2020-05-05 Midlands 58
## 277 2020-05-06 Midlands 59
## 278 2020-05-07 Midlands 48
## 279 2020-05-08 Midlands 34
## 280 2020-05-09 Midlands 37
## 281 2020-05-10 Midlands 42
## 282 2020-05-11 Midlands 33
## 283 2020-05-12 Midlands 45
## 284 2020-05-13 Midlands 39
## 285 2020-05-14 Midlands 37
## 286 2020-05-15 Midlands 40
## 287 2020-05-16 Midlands 34
## 288 2020-05-17 Midlands 31
## 289 2020-05-18 Midlands 34
## 290 2020-05-19 Midlands 34
## 291 2020-05-20 Midlands 36
## 292 2020-05-21 Midlands 32
## 293 2020-05-22 Midlands 27
## 294 2020-05-23 Midlands 34
## 295 2020-05-24 Midlands 19
## 296 2020-05-25 Midlands 26
## 297 2020-05-26 Midlands 33
## 298 2020-05-27 Midlands 29
## 299 2020-05-28 Midlands 27
## 300 2020-05-29 Midlands 20
## 301 2020-05-30 Midlands 20
## 302 2020-05-31 Midlands 21
## 303 2020-06-01 Midlands 20
## 304 2020-06-02 Midlands 21
## 305 2020-06-03 Midlands 23
## 306 2020-06-04 Midlands 15
## 307 2020-06-05 Midlands 21
## 308 2020-06-06 Midlands 19
## 309 2020-06-07 Midlands 14
## 310 2020-06-08 Midlands 15
## 311 2020-06-09 Midlands 17
## 312 2020-06-10 Midlands 14
## 313 2020-06-11 Midlands 13
## 314 2020-06-12 Midlands 6
## 315 2020-06-13 Midlands 0
## 316 2020-03-01 North East and Yorkshire 0
## 317 2020-03-02 North East and Yorkshire 0
## 318 2020-03-03 North East and Yorkshire 0
## 319 2020-03-04 North East and Yorkshire 0
## 320 2020-03-05 North East and Yorkshire 0
## 321 2020-03-06 North East and Yorkshire 0
## 322 2020-03-07 North East and Yorkshire 0
## 323 2020-03-08 North East and Yorkshire 0
## 324 2020-03-09 North East and Yorkshire 0
## 325 2020-03-10 North East and Yorkshire 0
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## 327 2020-03-12 North East and Yorkshire 0
## 328 2020-03-13 North East and Yorkshire 0
## 329 2020-03-14 North East and Yorkshire 0
## 330 2020-03-15 North East and Yorkshire 2
## 331 2020-03-16 North East and Yorkshire 3
## 332 2020-03-17 North East and Yorkshire 1
## 333 2020-03-18 North East and Yorkshire 2
## 334 2020-03-19 North East and Yorkshire 6
## 335 2020-03-20 North East and Yorkshire 5
## 336 2020-03-21 North East and Yorkshire 6
## 337 2020-03-22 North East and Yorkshire 7
## 338 2020-03-23 North East and Yorkshire 9
## 339 2020-03-24 North East and Yorkshire 8
## 340 2020-03-25 North East and Yorkshire 18
## 341 2020-03-26 North East and Yorkshire 21
## 342 2020-03-27 North East and Yorkshire 28
## 343 2020-03-28 North East and Yorkshire 35
## 344 2020-03-29 North East and Yorkshire 38
## 345 2020-03-30 North East and Yorkshire 64
## 346 2020-03-31 North East and Yorkshire 60
## 347 2020-04-01 North East and Yorkshire 67
## 348 2020-04-02 North East and Yorkshire 74
## 349 2020-04-03 North East and Yorkshire 100
## 350 2020-04-04 North East and Yorkshire 105
## 351 2020-04-05 North East and Yorkshire 92
## 352 2020-04-06 North East and Yorkshire 96
## 353 2020-04-07 North East and Yorkshire 102
## 354 2020-04-08 North East and Yorkshire 107
## 355 2020-04-09 North East and Yorkshire 111
## 356 2020-04-10 North East and Yorkshire 117
## 357 2020-04-11 North East and Yorkshire 98
## 358 2020-04-12 North East and Yorkshire 84
## 359 2020-04-13 North East and Yorkshire 94
## 360 2020-04-14 North East and Yorkshire 107
## 361 2020-04-15 North East and Yorkshire 96
## 362 2020-04-16 North East and Yorkshire 103
## 363 2020-04-17 North East and Yorkshire 88
## 364 2020-04-18 North East and Yorkshire 95
## 365 2020-04-19 North East and Yorkshire 88
## 366 2020-04-20 North East and Yorkshire 100
## 367 2020-04-21 North East and Yorkshire 76
## 368 2020-04-22 North East and Yorkshire 84
## 369 2020-04-23 North East and Yorkshire 63
## 370 2020-04-24 North East and Yorkshire 72
## 371 2020-04-25 North East and Yorkshire 69
## 372 2020-04-26 North East and Yorkshire 65
## 373 2020-04-27 North East and Yorkshire 65
## 374 2020-04-28 North East and Yorkshire 57
## 375 2020-04-29 North East and Yorkshire 69
## 376 2020-04-30 North East and Yorkshire 57
## 377 2020-05-01 North East and Yorkshire 64
## 378 2020-05-02 North East and Yorkshire 48
## 379 2020-05-03 North East and Yorkshire 40
## 380 2020-05-04 North East and Yorkshire 49
## 381 2020-05-05 North East and Yorkshire 40
## 382 2020-05-06 North East and Yorkshire 50
## 383 2020-05-07 North East and Yorkshire 45
## 384 2020-05-08 North East and Yorkshire 42
## 385 2020-05-09 North East and Yorkshire 44
## 386 2020-05-10 North East and Yorkshire 40
## 387 2020-05-11 North East and Yorkshire 29
## 388 2020-05-12 North East and Yorkshire 27
## 389 2020-05-13 North East and Yorkshire 28
## 390 2020-05-14 North East and Yorkshire 30
## 391 2020-05-15 North East and Yorkshire 32
## 392 2020-05-16 North East and Yorkshire 35
## 393 2020-05-17 North East and Yorkshire 26
## 394 2020-05-18 North East and Yorkshire 29
## 395 2020-05-19 North East and Yorkshire 27
## 396 2020-05-20 North East and Yorkshire 21
## 397 2020-05-21 North East and Yorkshire 33
## 398 2020-05-22 North East and Yorkshire 22
## 399 2020-05-23 North East and Yorkshire 18
## 400 2020-05-24 North East and Yorkshire 25
## 401 2020-05-25 North East and Yorkshire 21
## 402 2020-05-26 North East and Yorkshire 21
## 403 2020-05-27 North East and Yorkshire 21
## 404 2020-05-28 North East and Yorkshire 20
## 405 2020-05-29 North East and Yorkshire 24
## 406 2020-05-30 North East and Yorkshire 20
## 407 2020-05-31 North East and Yorkshire 19
## 408 2020-06-01 North East and Yorkshire 16
## 409 2020-06-02 North East and Yorkshire 22
## 410 2020-06-03 North East and Yorkshire 22
## 411 2020-06-04 North East and Yorkshire 17
## 412 2020-06-05 North East and Yorkshire 17
## 413 2020-06-06 North East and Yorkshire 20
## 414 2020-06-07 North East and Yorkshire 13
## 415 2020-06-08 North East and Yorkshire 11
## 416 2020-06-09 North East and Yorkshire 11
## 417 2020-06-10 North East and Yorkshire 15
## 418 2020-06-11 North East and Yorkshire 4
## 419 2020-06-12 North East and Yorkshire 7
## 420 2020-06-13 North East and Yorkshire 2
## 421 2020-03-01 North West 0
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## 425 2020-03-05 North West 1
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## 429 2020-03-09 North West 0
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## 438 2020-03-18 North West 6
## 439 2020-03-19 North West 7
## 440 2020-03-20 North West 10
## 441 2020-03-21 North West 11
## 442 2020-03-22 North West 13
## 443 2020-03-23 North West 16
## 444 2020-03-24 North West 21
## 445 2020-03-25 North West 21
## 446 2020-03-26 North West 29
## 447 2020-03-27 North West 35
## 448 2020-03-28 North West 28
## 449 2020-03-29 North West 46
## 450 2020-03-30 North West 67
## 451 2020-03-31 North West 52
## 452 2020-04-01 North West 86
## 453 2020-04-02 North West 96
## 454 2020-04-03 North West 95
## 455 2020-04-04 North West 98
## 456 2020-04-05 North West 102
## 457 2020-04-06 North West 100
## 458 2020-04-07 North West 134
## 459 2020-04-08 North West 127
## 460 2020-04-09 North West 119
## 461 2020-04-10 North West 117
## 462 2020-04-11 North West 139
## 463 2020-04-12 North West 126
## 464 2020-04-13 North West 129
## 465 2020-04-14 North West 131
## 466 2020-04-15 North West 114
## 467 2020-04-16 North West 134
## 468 2020-04-17 North West 98
## 469 2020-04-18 North West 113
## 470 2020-04-19 North West 71
## 471 2020-04-20 North West 83
## 472 2020-04-21 North West 76
## 473 2020-04-22 North West 86
## 474 2020-04-23 North West 85
## 475 2020-04-24 North West 66
## 476 2020-04-25 North West 65
## 477 2020-04-26 North West 55
## 478 2020-04-27 North West 54
## 479 2020-04-28 North West 57
## 480 2020-04-29 North West 62
## 481 2020-04-30 North West 59
## 482 2020-05-01 North West 45
## 483 2020-05-02 North West 56
## 484 2020-05-03 North West 55
## 485 2020-05-04 North West 48
## 486 2020-05-05 North West 48
## 487 2020-05-06 North West 44
## 488 2020-05-07 North West 49
## 489 2020-05-08 North West 42
## 490 2020-05-09 North West 30
## 491 2020-05-10 North West 41
## 492 2020-05-11 North West 34
## 493 2020-05-12 North West 38
## 494 2020-05-13 North West 25
## 495 2020-05-14 North West 26
## 496 2020-05-15 North West 33
## 497 2020-05-16 North West 32
## 498 2020-05-17 North West 24
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## 500 2020-05-19 North West 35
## 501 2020-05-20 North West 27
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## 503 2020-05-22 North West 26
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## 505 2020-05-24 North West 26
## 506 2020-05-25 North West 31
## 507 2020-05-26 North West 27
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## 514 2020-06-02 North West 27
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## 523 2020-06-11 North West 14
## 524 2020-06-12 North West 4
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## 530 2020-03-05 South East 1
## 531 2020-03-06 South East 0
## 532 2020-03-07 South East 0
## 533 2020-03-08 South East 1
## 534 2020-03-09 South East 1
## 535 2020-03-10 South East 1
## 536 2020-03-11 South East 1
## 537 2020-03-12 South East 0
## 538 2020-03-13 South East 1
## 539 2020-03-14 South East 1
## 540 2020-03-15 South East 5
## 541 2020-03-16 South East 8
## 542 2020-03-17 South East 7
## 543 2020-03-18 South East 10
## 544 2020-03-19 South East 9
## 545 2020-03-20 South East 14
## 546 2020-03-21 South East 7
## 547 2020-03-22 South East 25
## 548 2020-03-23 South East 20
## 549 2020-03-24 South East 22
## 550 2020-03-25 South East 29
## 551 2020-03-26 South East 34
## 552 2020-03-27 South East 34
## 553 2020-03-28 South East 36
## 554 2020-03-29 South East 54
## 555 2020-03-30 South East 58
## 556 2020-03-31 South East 65
## 557 2020-04-01 South East 66
## 558 2020-04-02 South East 55
## 559 2020-04-03 South East 72
## 560 2020-04-04 South East 80
## 561 2020-04-05 South East 82
## 562 2020-04-06 South East 88
## 563 2020-04-07 South East 100
## 564 2020-04-08 South East 83
## 565 2020-04-09 South East 104
## 566 2020-04-10 South East 88
## 567 2020-04-11 South East 88
## 568 2020-04-12 South East 88
## 569 2020-04-13 South East 84
## 570 2020-04-14 South East 65
## 571 2020-04-15 South East 72
## 572 2020-04-16 South East 56
## 573 2020-04-17 South East 86
## 574 2020-04-18 South East 57
## 575 2020-04-19 South East 70
## 576 2020-04-20 South East 86
## 577 2020-04-21 South East 50
## 578 2020-04-22 South East 54
## 579 2020-04-23 South East 57
## 580 2020-04-24 South East 64
## 581 2020-04-25 South East 51
## 582 2020-04-26 South East 51
## 583 2020-04-27 South East 40
## 584 2020-04-28 South East 40
## 585 2020-04-29 South East 47
## 586 2020-04-30 South East 29
## 587 2020-05-01 South East 37
## 588 2020-05-02 South East 36
## 589 2020-05-03 South East 17
## 590 2020-05-04 South East 35
## 591 2020-05-05 South East 29
## 592 2020-05-06 South East 25
## 593 2020-05-07 South East 27
## 594 2020-05-08 South East 26
## 595 2020-05-09 South East 28
## 596 2020-05-10 South East 19
## 597 2020-05-11 South East 25
## 598 2020-05-12 South East 27
## 599 2020-05-13 South East 18
## 600 2020-05-14 South East 32
## 601 2020-05-15 South East 24
## 602 2020-05-16 South East 22
## 603 2020-05-17 South East 18
## 604 2020-05-18 South East 22
## 605 2020-05-19 South East 12
## 606 2020-05-20 South East 22
## 607 2020-05-21 South East 14
## 608 2020-05-22 South East 17
## 609 2020-05-23 South East 21
## 610 2020-05-24 South East 16
## 611 2020-05-25 South East 13
## 612 2020-05-26 South East 19
## 613 2020-05-27 South East 17
## 614 2020-05-28 South East 12
## 615 2020-05-29 South East 18
## 616 2020-05-30 South East 8
## 617 2020-05-31 South East 10
## 618 2020-06-01 South East 11
## 619 2020-06-02 South East 12
## 620 2020-06-03 South East 17
## 621 2020-06-04 South East 11
## 622 2020-06-05 South East 9
## 623 2020-06-06 South East 9
## 624 2020-06-07 South East 11
## 625 2020-06-08 South East 5
## 626 2020-06-09 South East 9
## 627 2020-06-10 South East 8
## 628 2020-06-11 South East 3
## 629 2020-06-12 South East 5
## 630 2020-06-13 South East 0
## 631 2020-03-01 South West 0
## 632 2020-03-02 South West 0
## 633 2020-03-03 South West 0
## 634 2020-03-04 South West 0
## 635 2020-03-05 South West 0
## 636 2020-03-06 South West 0
## 637 2020-03-07 South West 0
## 638 2020-03-08 South West 0
## 639 2020-03-09 South West 0
## 640 2020-03-10 South West 0
## 641 2020-03-11 South West 1
## 642 2020-03-12 South West 0
## 643 2020-03-13 South West 0
## 644 2020-03-14 South West 1
## 645 2020-03-15 South West 0
## 646 2020-03-16 South West 0
## 647 2020-03-17 South West 2
## 648 2020-03-18 South West 2
## 649 2020-03-19 South West 5
## 650 2020-03-20 South West 3
## 651 2020-03-21 South West 6
## 652 2020-03-22 South West 9
## 653 2020-03-23 South West 9
## 654 2020-03-24 South West 7
## 655 2020-03-25 South West 9
## 656 2020-03-26 South West 11
## 657 2020-03-27 South West 13
## 658 2020-03-28 South West 21
## 659 2020-03-29 South West 18
## 660 2020-03-30 South West 23
## 661 2020-03-31 South West 23
## 662 2020-04-01 South West 22
## 663 2020-04-02 South West 23
## 664 2020-04-03 South West 30
## 665 2020-04-04 South West 42
## 666 2020-04-05 South West 32
## 667 2020-04-06 South West 34
## 668 2020-04-07 South West 39
## 669 2020-04-08 South West 47
## 670 2020-04-09 South West 24
## 671 2020-04-10 South West 46
## 672 2020-04-11 South West 43
## 673 2020-04-12 South West 23
## 674 2020-04-13 South West 27
## 675 2020-04-14 South West 24
## 676 2020-04-15 South West 32
## 677 2020-04-16 South West 29
## 678 2020-04-17 South West 33
## 679 2020-04-18 South West 25
## 680 2020-04-19 South West 31
## 681 2020-04-20 South West 26
## 682 2020-04-21 South West 26
## 683 2020-04-22 South West 23
## 684 2020-04-23 South West 17
## 685 2020-04-24 South West 19
## 686 2020-04-25 South West 15
## 687 2020-04-26 South West 27
## 688 2020-04-27 South West 13
## 689 2020-04-28 South West 17
## 690 2020-04-29 South West 15
## 691 2020-04-30 South West 26
## 692 2020-05-01 South West 6
## 693 2020-05-02 South West 7
## 694 2020-05-03 South West 10
## 695 2020-05-04 South West 17
## 696 2020-05-05 South West 14
## 697 2020-05-06 South West 19
## 698 2020-05-07 South West 16
## 699 2020-05-08 South West 6
## 700 2020-05-09 South West 11
## 701 2020-05-10 South West 5
## 702 2020-05-11 South West 8
## 703 2020-05-12 South West 7
## 704 2020-05-13 South West 7
## 705 2020-05-14 South West 6
## 706 2020-05-15 South West 4
## 707 2020-05-16 South West 4
## 708 2020-05-17 South West 6
## 709 2020-05-18 South West 4
## 710 2020-05-19 South West 6
## 711 2020-05-20 South West 1
## 712 2020-05-21 South West 9
## 713 2020-05-22 South West 6
## 714 2020-05-23 South West 6
## 715 2020-05-24 South West 3
## 716 2020-05-25 South West 8
## 717 2020-05-26 South West 11
## 718 2020-05-27 South West 5
## 719 2020-05-28 South West 9
## 720 2020-05-29 South West 4
## 721 2020-05-30 South West 3
## 722 2020-05-31 South West 2
## 723 2020-06-01 South West 6
## 724 2020-06-02 South West 2
## 725 2020-06-03 South West 5
## 726 2020-06-04 South West 2
## 727 2020-06-05 South West 2
## 728 2020-06-06 South West 1
## 729 2020-06-07 South West 3
## 730 2020-06-08 South West 3
## 731 2020-06-09 South West 0
## 732 2020-06-10 South West 0
## 733 2020-06-11 South West 2
## 734 2020-06-12 South West 2
## 735 2020-06-13 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Sunday 14 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.7147 -2.1978 -0.4288 2.3486 4.4971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.017e+00 5.240e-02 95.75 <2e-16 ***
## note_lag 1.106e-05 5.149e-07 21.48 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 9.547593)
##
## Null deviance: 4757.11 on 43 degrees of freedom
## Residual deviance: 413.95 on 42 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 150.975634 1.000011
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 136.09359 167.128405
## note_lag 1.00001 1.000012
Rsq(lag_mod)
## [1] 0.9129832
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.4.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.5.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.2
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0